矿物分类系统开发笔记（一）：数据预处理

一、数据基础与预处理目标

二、具体预处理步骤及代码解析

2.1 数据加载与初步清洗

2.2 标签编码

2.3 缺失值处理

（1）删除含缺失值的样本

（2）按类别均值填充

（3）按类别中位数填充

（4）按类别众数填充

（5）线性回归填充

（6）随机森林填充

2.4 特征标准化

2.5 数据集拆分与类别平衡

（1）拆分训练集与测试集

（2）处理类别不平衡

2.6 数据保存

三、具体代码

四、预处理小结

一、数据基础与预处理目标

本矿物分类系统基于矿物数据.xlsx展开，该数据包含 1044 条样本，涵盖 13 种元素特征（氯、钠、镁等）和 4 类矿物标签（A、B、C、D）。因数据敏感，故无法提供，仅提供代码用于学习。数据预处理的核心目标是：通过规范格式、处理缺失值、平衡类别等操作，为后续模型训练提供可靠输入。

二、具体预处理步骤及代码解析

2.1 数据加载与初步清洗

首先加载数据并剔除无效信息：

import pandas as pd# 加载数据，仅保留有效样本
data = pd.read_excel('矿物数据.xlsx', sheet_name='Sheet1')
# 删除特殊类别E（仅1条样本，无统计意义）
data = data[data['矿物类型'] != 'E']
# 转换数据类型，将非数值符号（如"/"、空格）转为缺失值NaN
for col in data.columns:if col not in ['序号', '矿物类型']:  # 标签列不转换# errors='coerce'确保非数值转为NaNdata[col] = pd.to_numeric(data[col], errors='coerce')

此步骤解决了原始数据中格式混乱的问题，确保所有特征列均为数值型，为后续处理奠定基础。

2.2 标签编码

将字符标签（A/B/C/D）转为模型可识别的整数：

# 建立标签映射关系
label_dict = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
# 转换标签并保持DataFrame格式
data['矿物类型'] = data['矿物类型'].map(label_dict)
# 分离特征与标签
X = data.drop(['序号', '矿物类型'], axis=1)  # 特征集
y = data['矿物类型']  # 标签集

编码后，标签范围为 0-3，符合机器学习模型对输入格式的要求。

2.3 缺失值处理

针对数据中存在的缺失值，设计了 6 种处理方案，具体如下：

（1）删除含缺失值的样本

适用于缺失率极低（<1%）的场景，直接剔除无效样本：

def drop_missing(train_data, train_label):# 合并特征与标签，便于按行删除combined = pd.concat([train_data, train_label], axis=1)combined = combined.reset_index(drop=True)  # 重置索引，避免删除后索引混乱cleaned = combined.dropna()  # 删除含缺失值的行# 分离特征与标签return cleaned.drop('矿物类型', axis=1), cleaned['矿物类型']

该方法优点是无偏差，缺点是可能丢失有效信息（当缺失率较高时）。

（2）按类别均值填充

对数值型特征，按矿物类型分组计算均值，用组内均值填充缺失值（减少跨类别干扰）：

def mean_fill(train_data, train_label):combined = pd.concat([train_data, train_label], axis=1)combined = combined.reset_index(drop=True)# 按矿物类型分组填充filled_groups = []for type_id in combined['矿物类型'].unique():group = combined[combined['矿物类型'] == type_id]# 计算组内各特征均值，用于填充该组缺失值filled_group = group.fillna(group.mean())filled_groups.append(filled_group)# 合并各组数据filled = pd.concat(filled_groups, axis=0).reset_index(drop=True)return filled.drop('矿物类型', axis=1), filled['矿物类型']

适用于特征分布较均匀的场景，避免了不同类别间的均值混淆。

（3）按类别中位数填充

当特征存在极端值（如个别样本钠含量远高于均值）时，用中位数填充更稳健：

def median_fill(train_data, train_label):combined = pd.concat([train_data, train_label], axis=1)combined = combined.reset_index(drop=True)filled_groups = []for type_id in combined['矿物类型'].unique():group = combined[combined['矿物类型'] == type_id]# 中位数对极端值不敏感filled_group = group.fillna(group.median())filled_groups.append(filled_group)filled = pd.concat(filled_groups, axis=0).reset_index(drop=True)return filled.drop('矿物类型', axis=1), filled['矿物类型']

（4）按类别众数填充

针对离散型特征（如部分元素含量为整数编码），采用众数（出现次数最多的值）填充：

def mode_fill(train_data, train_label):combined = pd.concat([train_data, train_label], axis=1)combined = combined.reset_index(drop=True)filled_groups = []for type_id in combined['矿物类型'].unique():group = combined[combined['矿物类型'] == type_id]# 对每列取众数，无众数时返回Nonefill_values = group.apply(lambda x: x.mode().iloc[0] if not x.mode().empty else None)filled_group = group.fillna(fill_values)filled_groups.append(filled_group)filled = pd.concat(filled_groups, axis=0).reset_index(drop=True)return filled.drop('矿物类型', axis=1), filled['矿物类型']

（5）线性回归填充

利用特征间的线性相关性（如氯与钠含量的关联）预测缺失值：

from sklearn.linear_model import LinearRegressiondef linear_reg_fill(train_data, train_label):combined = pd.concat([train_data, train_label], axis=1)features = combined.drop('矿物类型', axis=1)# 按缺失值数量升序处理（从缺失少的列开始）null_counts = features.isnull().sum().sort_values()for col in null_counts.index:if null_counts[col] == 0:continue  # 无缺失值则跳过# 构建训练数据：用其他特征预测当前列X_train = features.drop(col, axis=1).dropna()  # 其他特征无缺失的样本y_train = features.loc[X_train.index, col]  # 当前列的非缺失值# 待填充样本（当前列缺失，其他特征完整）X_pred = features.drop(col, axis=1).loc[features[col].isnull()]# 训练线性回归模型lr = LinearRegression()lr.fit(X_train, y_train)# 预测并填充缺失值features.loc[features[col].isnull(), col] = lr.predict(X_pred)return features, combined['矿物类型']

该方法要求特征间存在一定线性关系，适用于元素含量呈比例关联的场景。

（6）随机森林填充

对于特征间非线性关系，采用随机森林模型预测缺失值：

from sklearn.ensemble import RandomForestRegressordef rf_fill(train_data, train_label):combined = pd.concat([train_data, train_label], axis=1)features = combined.drop('矿物类型', axis=1)null_counts = features.isnull().sum().sort_values()for col in null_counts.index:if null_counts[col] == 0:continue# 分离训练样本和待填充样本X_train = features.drop(col, axis=1).dropna()y_train = features.loc[X_train.index, col]X_pred = features.drop(col, axis=1).loc[features[col].isnull()]# 训练随机森林回归器（100棵树，固定随机种子确保结果可复现）rfr = RandomForestRegressor(n_estimators=100, random_state=10)rfr.fit(X_train, y_train)# 填充预测结果features.loc[features[col].isnull(), col] = rfr.predict(X_pred)return features, combined['矿物类型']

随机森林能捕捉特征间复杂关系，填充精度通常高于线性方法，但计算成本略高。

2.4 特征标准化

不同元素含量数值差异大（如钠可达上千，硒多为 0-1），需消除量纲影响：

from sklearn.preprocessing import StandardScalerdef standardize_features(X_train, X_test):# 用训练集的均值和标准差进行标准化（避免测试集信息泄露）scaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)  # 拟合训练集并转换X_test_scaled = scaler.transform(X_test)  # 用相同参数转换测试集# 转回DataFrame格式，保留特征名称return pd.DataFrame(X_train_scaled, columns=X_train.columns), pd.DataFrame(X_test_scaled, columns=X_test.columns)

标准化后，所有特征均值为 0、标准差为 1，确保模型不受数值大小干扰。

2.5 数据集拆分与类别平衡

（1）拆分训练集与测试集

按 7:3 比例拆分，保持类别分布一致：

from sklearn.model_selection import train_test_split# stratify=y确保测试集与原始数据类别比例一致
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, stratify=y
)

（2）处理类别不平衡

采用 SMOTE 算法生成少数类样本，平衡各类别数量：

from imblearn.over_sampling import SMOTE# 仅对训练集过采样（测试集保持原始分布）
smote = SMOTE(k_neighbors=1, random_state=0)  # 近邻数=1，避免引入过多噪声
X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)

2.6 数据保存

将预处理后的数据存储，供后续模型训练使用：

def save_processed_data(X_train, y_train, X_test, y_test, method):# 拼接特征与标签train_df = pd.concat([X_train, pd.DataFrame(y_train, columns=['矿物类型'])], axis=1)test_df = pd.concat([X_test, pd.DataFrame(y_test, columns=['矿物类型'])], axis=1)# 保存为Excel，明确标识预处理方法train_df.to_excel(f'训练集_{method}.xlsx', index=False)test_df.to_excel(f'测试集_{method}.xlsx', index=False)# 示例：保存经随机森林填充和标准化的数据
save_processed_data(X_train_balanced, y_train_balanced, X_test, y_test, 'rf_fill_standardized')

三、具体代码

数据预处理.py：

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import fill_datadata = pd.read_excel('矿物数据.xlsx')
data = data[data['矿物类型'] != 'E']  # 删除特殊类别E，整个数据集中只存在1个E数据
null_num = data.isnull()
null_total = data.isnull().sum()X_whole = data.drop('矿物类型', axis=1).drop('序号', axis=1)
y_whole = data.矿物类型'''将数据中的中文标签转换为字符'''
label_dict = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
encoded_labels = [label_dict[label] for label in y_whole]
y_whole = pd.DataFrame(encoded_labels, columns=['矿物类型'])'''字符串数据转换成float，异常数据（'\'和空格）转换成nan'''
for column_name in X_whole.columns:X_whole[column_name] = pd.to_numeric(X_whole[column_name], errors='coerce')'''Z标准化'''
scaler = StandardScaler()
X_whole_Z = scaler.fit_transform(X_whole)
X_whole = pd.DataFrame(X_whole_Z, columns=X_whole.columns)  # Z标准化处理后为numpy数据，这里再转换回pandas数据'''数据集切分'''
X_train_w, X_test_w, y_train_w, y_test_w = train_test_split(X_whole, y_whole, test_size=0.3, random_state=0)'''数据填充，6种方法'''
# # 1.删除空缺行
# X_train_fill, y_train_fill = fill_data.cca_train_fill(X_train_w, y_train_w)
# X_test_fill, y_test_fill = fill_data.cca_test_fill(X_test_w, y_test_w)
# os_x_train, os_y_train = fill_data.oversampling(X_train_fill, y_train_fill)
# fill_data.cca_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill)
#
# # 2.平均值填充
# X_train_fill, y_train_fill = fill_data.mean_train_fill(X_train_w, y_train_w)
# X_test_fill, y_test_fill = fill_data.mean_test_fill(X_train_fill, y_train_fill, X_test_w, y_test_w)
# os_x_train, os_y_train = fill_data.oversampling(X_train_fill, y_train_fill)
# fill_data.mean_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill)
#
# # 3.中位数填充
# X_train_fill, y_train_fill = fill_data.median_train_fill(X_train_w, y_train_w)
# X_test_fill, y_test_fill = fill_data.median_test_fill(X_train_fill, y_train_fill, X_test_w, y_test_w)
# os_x_train, os_y_train = fill_data.oversampling(X_train_fill, y_train_fill)
# fill_data.median_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill)# # 4.众数填充
# X_train_fill, y_train_fill = fill_data.mode_train_fill(X_train_w, y_train_w)
# X_test_fill, y_test_fill = fill_data.mode_test_fill(X_train_fill, y_train_fill, X_test_w, y_test_w)
# os_x_train, os_y_train = fill_data.oversampling(X_train_fill, y_train_fill)
# fill_data.mode_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill)# # 5.线性回归填充
# X_train_fill, y_train_fill = fill_data.linear_train_fill(X_train_w, y_train_w)
# X_test_fill, y_test_fill = fill_data.linear_test_fill(X_train_fill, y_train_fill, X_test_w, y_test_w)
# os_x_train, os_y_train = fill_data.oversampling(X_train_fill, y_train_fill)
# fill_data.linear_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill)# 6.随机森林填充
X_train_fill, y_train_fill = fill_data.RandomForest_train_fill(X_train_w, y_train_w)
X_test_fill, y_test_fill = fill_data.RandomForest_test_fill(X_train_fill, y_train_fill, X_test_w, y_test_w)
os_x_train, os_y_train = fill_data.oversampling(X_train_fill, y_train_fill)
fill_data.RandomForest_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill)

fill_data.py：

import pandas as pd
from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression'''过采样'''
def oversampling(train_data, train_label):oversampler = SMOTE(k_neighbors=1, random_state=0)os_x_train, os_y_train = oversampler.fit_resample(train_data, train_label)return os_x_train, os_y_train'''1.删除空缺行'''
def cca_train_fill(train_data, train_label):data = pd.concat([train_data, train_label], axis=1)data = data.reset_index(drop=True)  # 重置索引df_filled = data.dropna()  # 删除包含缺失值的行或列return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def cca_test_fill(test_data, test_label):data = pd.concat([test_data, test_label], axis=1)data = data.reset_index(drop=True)df_filled = data.dropna()return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def cca_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill):data_train = pd.concat([os_x_train, os_y_train], axis=1)data_test = pd.concat([X_test_fill, y_test_fill], axis=1)data_train.to_excel(r'..//temp_data//训练数据集[删除空缺行].xlsx', index=False)data_test.to_excel(r'..//temp_data//测试数据集[删除空缺行].xlsx', index=False)'''2.平均值填充'''
def mean_train_method(data):fill_values = data.mean()return data.fillna(fill_values)def mean_test_method(train_data, test_data):fill_values = train_data.mean()return test_data.fillna(fill_values)def mean_train_fill(train_data, train_label):data = pd.concat([train_data, train_label], axis=1)data = data.reset_index(drop=True)A = data[data['矿物类型'] == 0]B = data[data['矿物类型'] == 1]C = data[data['矿物类型'] == 2]D = data[data['矿物类型'] == 3]A = mean_train_method(A)B = mean_train_method(B)C = mean_train_method(C)D = mean_train_method(D)df_filled = pd.concat([A, B, C, D], axis=0)df_filled = df_filled.reset_index(drop=True)return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def mean_test_fill(train_data, train_label, test_data, test_label):train_data_all = pd.concat([train_data, train_label], axis=1)train_data_all = train_data_all.reset_index(drop=True)test_data_all = pd.concat([test_data, test_label], axis=1)test_data_all = test_data_all.reset_index(drop=True)A_train = train_data_all[train_data_all['矿物类型'] == 0]B_train = train_data_all[train_data_all['矿物类型'] == 1]C_train = train_data_all[train_data_all['矿物类型'] == 2]D_train = train_data_all[train_data_all['矿物类型'] == 3]A_test = test_data_all[test_data_all['矿物类型'] == 0]B_test = test_data_all[test_data_all['矿物类型'] == 1]C_test = test_data_all[test_data_all['矿物类型'] == 2]D_test = test_data_all[test_data_all['矿物类型'] == 3]A = mean_test_method(A_train, A_test)B = mean_test_method(B_train, B_test)C = mean_test_method(C_train, C_test)D = mean_test_method(D_train, D_test)df_filled = pd.concat([A, B, C, D], axis=0)df_filled = df_filled.reset_index(drop=True)return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def mean_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill):data_train = pd.concat([os_x_train, os_y_train], axis=1)data_test = pd.concat([X_test_fill, y_test_fill], axis=1)data_train.to_excel(r'..//temp_data//训练数据集[平均值填充].xlsx', index=False)data_test.to_excel(r'..//temp_data//测试数据集[平均值填充].xlsx', index=False)'''3.中位数填充'''
def median_train_method(data):fill_values = data.median()return data.fillna(fill_values)def median_test_method(train_data, test_data):fill_values = train_data.median()return test_data.fillna(fill_values)def median_train_fill(train_data, train_label):data = pd.concat([train_data, train_label], axis=1)data = data.reset_index(drop=True)A = data[data['矿物类型'] == 0]B = data[data['矿物类型'] == 1]C = data[data['矿物类型'] == 2]D = data[data['矿物类型'] == 3]A = median_train_method(A)B = median_train_method(B)C = median_train_method(C)D = median_train_method(D)df_filled = pd.concat([A, B, C, D], axis=0)df_filled = df_filled.reset_index(drop=True)return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def median_test_fill(train_data, train_label, test_data, test_label):train_data_all = pd.concat([train_data, train_label], axis=1)train_data_all = train_data_all.reset_index(drop=True)test_data_all = pd.concat([test_data, test_label], axis=1)test_data_all = test_data_all.reset_index(drop=True)A_train = train_data_all[train_data_all['矿物类型'] == 0]B_train = train_data_all[train_data_all['矿物类型'] == 1]C_train = train_data_all[train_data_all['矿物类型'] == 2]D_train = train_data_all[train_data_all['矿物类型'] == 3]A_test = test_data_all[test_data_all['矿物类型'] == 0]B_test = test_data_all[test_data_all['矿物类型'] == 1]C_test = test_data_all[test_data_all['矿物类型'] == 2]D_test = test_data_all[test_data_all['矿物类型'] == 3]A = median_test_method(A_train, A_test)B = median_test_method(B_train, B_test)C = median_test_method(C_train, C_test)D = median_test_method(D_train, D_test)df_filled = pd.concat([A, B, C, D], axis=0)df_filled = df_filled.reset_index(drop=True)return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def median_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill):data_train = pd.concat([os_x_train, os_y_train], axis=1)data_test = pd.concat([X_test_fill, y_test_fill], axis=1)data_train.to_excel(r'..//temp_data//训练数据集[中位数填充].xlsx', index=False)data_test.to_excel(r'..//temp_data//测试数据集[中位数填充].xlsx', index=False)'''4.众数填充'''
def mode_train_method(data):fill_values = data.apply(lambda x: x.mode().iloc[0] if len(x.mode()) > 0 else None)a = data.mode()return data.fillna(fill_values)def mode_test_method(train_data, test_data):fill_values = train_data.apply(lambda x: x.mode().iloc[0] if len(x.mode()) > 0 else None)a = train_data.mode()return test_data.fillna(fill_values)def mode_train_fill(train_data, train_label):data = pd.concat([train_data, train_label], axis=1)data = data.reset_index(drop=True)A = data[data['矿物类型'] == 0]B = data[data['矿物类型'] == 1]C = data[data['矿物类型'] == 2]D = data[data['矿物类型'] == 3]A = mode_train_method(A)B = mode_train_method(B)C = mode_train_method(C)D = mode_train_method(D)df_filled = pd.concat([A, B, C, D], axis=0)df_filled = df_filled.reset_index(drop=True)return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def mode_test_fill(train_data, train_label, test_data, test_label):train_data_all = pd.concat([train_data, train_label], axis=1)train_data_all = train_data_all.reset_index(drop=True)test_data_all = pd.concat([test_data, test_label], axis=1)test_data_all = test_data_all.reset_index(drop=True)A_train = train_data_all[train_data_all['矿物类型'] == 0]B_train = train_data_all[train_data_all['矿物类型'] == 1]C_train = train_data_all[train_data_all['矿物类型'] == 2]D_train = train_data_all[train_data_all['矿物类型'] == 3]A_test = test_data_all[test_data_all['矿物类型'] == 0]B_test = test_data_all[test_data_all['矿物类型'] == 1]C_test = test_data_all[test_data_all['矿物类型'] == 2]D_test = test_data_all[test_data_all['矿物类型'] == 3]A = mode_test_method(A_train, A_test)B = mode_test_method(B_train, B_test)C = mode_test_method(C_train, C_test)D = mode_test_method(D_train, D_test)df_filled = pd.concat([A, B, C, D], axis=0)df_filled = df_filled.reset_index(drop=True)return df_filled.drop('矿物类型', axis=1), df_filled.矿物类型def mode_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill):data_train = pd.concat([os_x_train, os_y_train], axis=1)data_test = pd.concat([X_test_fill, y_test_fill], axis=1)data_train.to_excel(r'..//temp_data//训练数据集[众数填充].xlsx', index=False)data_test.to_excel(r'..//temp_data//测试数据集[众数填充].xlsx', index=False)'''5.线性回归填充'''
def linear_train_fill(train_data, train_label):train_data_all = pd.concat([train_data, train_label], axis=1)train_data_all = train_data_all.reset_index(drop=True)train_data_X = train_data_all.drop('矿物类型', axis=1)null_num = train_data_X.isnull().sum()null_num_sorted = null_num.sort_values(ascending=True)filling_feature = []for i in null_num_sorted.index:filling_feature.append(i)if null_num_sorted[i] != 0:X = train_data_X[filling_feature].drop(i, axis=1)y = train_data_X[i]row_numbers_mg_null = train_data_X[train_data_X[i].isnull()].index.tolist()X_train = X.drop(row_numbers_mg_null)y_train = y.drop(row_numbers_mg_null)X_test = X.iloc[row_numbers_mg_null]lr = LinearRegression()lr.fit(X_train, y_train)y_pred = lr.predict(X_test)train_data_X.loc[row_numbers_mg_null, i] = y_predprint(f'完成训练数据集中的{i}列数据的填充')return train_data_X, train_data_all.矿物类型def linear_test_fill(train_data, train_label, test_data, test_label):train_data_all = pd.concat([train_data, train_label], axis=1)train_data_all = train_data_all.reset_index(drop=True)test_data_all = pd.concat([test_data, test_label], axis=1)test_data_all = test_data_all.reset_index(drop=True)train_data_X = train_data_all.drop('矿物类型', axis=1)test_data_X = test_data_all.drop('矿物类型', axis=1)null_num = test_data_X.isnull().sum()null_num_sorted = null_num.sort_values(ascending=True)filling_feature = []for i in null_num_sorted.index:filling_feature.append(i)if null_num_sorted[i] != 0:X_train = train_data_X[filling_feature].drop(i, axis=1)y_train = train_data_X[i]X_test  = test_data_X[filling_feature].drop(i, axis=1)row_numbers_mg_null = test_data_X[test_data_X[i].isnull()].index.tolist()X_test = X_test.iloc[row_numbers_mg_null]lr = LinearRegression()lr.fit(X_train, y_train)y_pred = lr.predict(X_test)test_data_X.loc[row_numbers_mg_null, i] = y_predprint(f'完成测试数据集中的{i}列数据的填充')return test_data_X, test_data_all.矿物类型def linear_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill):data_train = pd.concat([os_x_train, os_y_train], axis=1)data_test = pd.concat([X_test_fill, y_test_fill], axis=1)data_train.to_excel(r'..//temp_data//训练数据集[线性回归填充].xlsx', index=False)data_test.to_excel(r'..//temp_data//测试数据集[线性回归填充].xlsx', index=False)'''6.随机森林填充'''
def RandomForest_train_fill(train_data, train_label):train_data_all = pd.concat([train_data, train_label], axis=1)train_data_all = train_data_all.reset_index(drop=True)train_data_X = train_data_all.drop('矿物类型', axis=1)null_num = train_data_X.isnull().sum()null_num_sorted = null_num.sort_values(ascending=True)filling_feature = []for i in null_num_sorted.index:filling_feature.append(i)if null_num_sorted[i] != 0:X = train_data_X[filling_feature].drop(i, axis=1)y = train_data_X[i]row_numbers_mg_null = train_data_X[train_data_X[i].isnull()].index.tolist()X_train = X.drop(row_numbers_mg_null)y_train = y.drop(row_numbers_mg_null)X_test = X.iloc[row_numbers_mg_null]rfg = RandomForestRegressor(n_estimators=100, random_state=10)rfg.fit(X_train, y_train)y_pred = rfg.predict(X_test)train_data_X.loc[row_numbers_mg_null, i] = y_predprint(f'完成训练数据集中的{i}列数据的填充')return train_data_X, train_data_all.矿物类型def RandomForest_test_fill(train_data, train_label, test_data, test_label):train_data_all = pd.concat([train_data, train_label], axis=1)train_data_all = train_data_all.reset_index(drop=True)test_data_all = pd.concat([test_data, test_label], axis=1)test_data_all = test_data_all.reset_index(drop=True)train_data_X = train_data_all.drop('矿物类型', axis=1)test_data_X = test_data_all.drop('矿物类型', axis=1)null_num = test_data_X.isnull().sum()null_num_sorted = null_num.sort_values(ascending=True)filling_feature = []for i in null_num_sorted.index:filling_feature.append(i)if null_num_sorted[i] != 0:X_train = train_data_X[filling_feature].drop(i, axis=1)y_train = train_data_X[i]X_test  = test_data_X[filling_feature].drop(i, axis=1)row_numbers_mg_null = test_data_X[test_data_X[i].isnull()].index.tolist()X_test = X_test.iloc[row_numbers_mg_null]rfg = RandomForestRegressor(n_estimators=100, random_state=10)rfg.fit(X_train, y_train)y_pred = rfg.predict(X_test)test_data_X.loc[row_numbers_mg_null, i] = y_predprint(f'完成测试数据集中的{i}列数据的填充')return test_data_X, test_data_all.矿物类型def RandomForest_save_file(os_x_train, os_y_train, X_test_fill, y_test_fill):data_train = pd.concat([os_x_train, os_y_train], axis=1)data_test = pd.concat([X_test_fill, y_test_fill], axis=1)data_train.to_excel(r'..//temp_data//训练数据集[随机森林填充].xlsx', index=False)data_test.to_excel(r'..//temp_data//测试数据集[随机森林填充].xlsx', index=False)